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Shock and Vibration
Volume 16, Issue 6, Pages 607-621
http://dx.doi.org/10.3233/SAV-2009-0492

A State-Space Modeling Approach for Active Structural Acoustic Control

Leopoldo P.R. de Oliveira,1,2 Paulo S. Varoto,1 Paul Sas,2 and Wim Desmet2

1University of São Paulo, Engineering School of São Carlos, Dynamics Lab. Av. Trabalhador Sancarlense, 400, 13566-590 São Carlos-SP, Brazil
2Katholieke Universiteit Leuven, Department of Mechanical Engineering, Celestijnenlaan 300B, 3000 Leuven, Belgium

Received 27 March 2008; Revised 23 December 2008

Copyright © 2009 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The demands for improvement in sound quality and reduction of noise generated by vehicles are constantly increasing, as well as the penalties for space and weight of the control solutions. A promising approach to cope with this challenge is the use of active structural-acoustic control. Usually, the low frequency noise is transmitted into the vehicle's cabin through structural paths, which raises the necessity of dealing with vibro-acoustic models. This kind of models should allow the inclusion of sensors and actuators models, if accurate performance indexes are to be accessed. The challenge thus resides in deriving reasonable sized models that integrate structural, acoustic, electrical components and the controller algorithm. The advantages of adequate active control simulation strategies relies on the cost and time reduction in the development phase. Therefore, the aim of this paper is to present a methodology for simulating vibro-acoustic systems including this coupled model in a closed loop control simulation framework that also takes into account the interaction between the system and the control sensors/actuators. It is shown that neglecting the sensor/actuator dynamics can lead to inaccurate performance predictions.